A physics-informed feature weighting method for bearing fault diagnostics
A physics-informed feature weighting method for bearing fault diagnostics
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DOI：
10.1016/j.ymssp.2023.110171
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发表时间：
2023-02-07
影响因子：
8.4
通讯作者：
Zimmerman, Andrew T.
中科院分区：
文献类型：
--
作者：
Lu, Hao;Nemani, Venkat Pavan;Zimmerman, Andrew T.
智能轴承诊断在过去几年中变得流行起来。然而，大多数诊断方法都是在假设训练和测试数据集是在相同的工作条件下收集的基础上开发的。这种假设在实际情况中很少见，因为旋转机械通常在很大的转速和负载范围内工作。由于轴承在复杂且时变的运行条件下工作，测试数据可能来自训练分布之外的数据分布。纯粹的数据驱动的诊断模型往往不能为分散的测试数据提供可靠的分类。针对这一挑战，本文提出了一种基于物理信息的轴承诊断特征加权方法。首先，提出了一个信号处理步骤，该步骤利用轴承故障的物理知识来提取对轴承速度变化具有鲁棒性的区分性特征。在此基础上，提出了一种新的物理信息特征加权层，为更接近轴承故障特征频率的特征赋予更高的权重。特征加权层提高了模型对速度不变特征中故障相关特征的敏感度。通过对三个轴承数据集的实验，验证了该方法的有效性，并表明该方法在不同运行条件下的轴承故障诊断中具有良好的应用前景。本研究还详细介绍了在工业物联网(IIoT)设备上部署物理信息卷积神经网络模型，其中边缘计算为用户提供轴承健康的实时评估。
Intelligent bearing diagnostics has gained popularity over the last few years. However, most of the diagnostic methods are developed under the assumption that training and test data sets are collected under the same working conditions. This assumption is rare in practical scenarios because rotating machinery usually works under wide ranges of rotational speeds and loads. As bearings work under complex and time-varying operating conditions, the test data might come from a data distribution outside the training distribution. Purely data-driven diagnostic models often cannot provide reliable classifications for out-of-distribution test data. To tackle this chal-lenge, this paper proposes a physics-informed feature weighting method for bearing diagnostics. First, a signal processing step is proposed that leverages physical knowledge of bearing faults to extract discriminative features that are robust to bearing speed variation. Then, a novel physics-informed feature weighting layer is developed to assign higher weights for features located closer to bearing fault characteristic frequencies. The feature weighting layer enhances the model's sensitivity towards the fault-related features among the speed invariant features. Through ex-periments on three bearing datasets, the effectiveness of the proposed method is validated and shown to have promise for bearing fault diagnostics under different operating conditions. This study also details the deployment of a physics-informed convolutional neural network model on an Industrial Internet of Things (IIoT) device, where edge computing gives users a real-time evaluation of bearing health.